The 2006 PASCAL Visual Object
Classes Challenge
Mark Everingham
Luc Van Gool
Chris Williams
Andrew Zisserman
Challenge
• Ten object classes
– bicycle, bus, car, cat, cow, dog, horse, motorbike,
person, sheep
• Classification
– Predict whether at least one object of a given class is
present
• Detection
– Predict bounding boxes of objects of a given class
Competitions
• Train on the supplied data
– Which methods perform best given specified training
data?
• Train on any (non-test) data
– How well do state-of-the-art methods perform on
these problems?
– Which methods perform best?
Dataset
• Images taken from three sources
– Personal photos contributed by Edinburgh/Oxford
– Microsoft Research Cambridge images
– Images taken from “flickr” photo-sharing website
• Annotation
– Bounding box
– Viewpoint: front, rear, left, right, unspecified
– “Truncated” flag: Bounding box ≠ object extent
– “Difficult” flag: Objects ignored in challenge
Examples
Bicycle
Car
Bus
Cat
Cow
Dog
Motorbike
Horse
Person
Sheep
Annotation Procedure
• All annotation performed in a single session in a single
location by seven annotators
• Detailed guidelines decided beforehand
– What to label
• Not excessive motion blur, poor illumination etc.
• Object size, “recognisability”, level of occlusion
• “Close-fitting occluders” e.g. snow/mud treated as object
• Through glass, mirrors, pictures: label, reflections (=occlusion)
• Non-photorealistic pictures: don’t label
– Viewpoint
– Bounding box e.g. don’t extend greatly for few pixels
– Truncation: significant amount of object outside bounding box
• “Difficult” flag set afterwards by a single annotator
examining individual objects in isolation
Dataset Statistics
train
val
trainval
test
img
obj
img
obj
img
obj
img
obj
Bicycle 127
161
143
162
270
323
268
326
Bus
93
118
81
117
174
235
180
233
Car 271
427
282
427
553
854
544
854
Cat 192
214
194
215
386
429
388
429
Cow 102
156
104
157
206
313
197
315
Dog 189
211
176
211
365
422
370
423
Horse 129
164
118
162
247
326
254
324
Motorbike 118
138
117
137
23